We present a novel technique for modulating the appearance frequency of a few tokens within a dataset for encoding an invisible watermark that can be used to protect ownership rights upon data. We develop optimal as well as fast heuristic algorithms for creating and verifying such watermarks. We also demonstrate the robustness of our technique against various attacks and derive analytical bounds for the false positive probability of erroneously “detecting” a watermark on a dataset that does not carry it. Our technique is applicable to both single dimensional and multidimensional datasets, is independent of token type, allows for a fine control of the introduced distortion, and can be used in a variety of use cases that involve buying and selling data in contemporary data marketplaces.

In the last decade we have witnessed a more than prolific growth of online social media content in sites designed for online social interactions. These systems have been traditionally designed as centralized silos, which unfortunately suffer from abusive behavior ranging from spam, cyberbullying to even censorship. This paper investigates the utility of supervised learning techniques for abuse detection in future decentralized settings, where less metadata remains available for use in learning algorithms. We present a method that uses a privacy-preserving protocol to exchange a fingerprint of the neighborhood of a pair of nodes, namely sender and receiver. Our method extracts social graph metadata to form a subset of key features, namely neighborhood knowledge, some of which we approximate to reduce communication and computational requirements of such a protocol. In our benchmarking we show that a data minimization approach can obtain features 13% faster while providing similar or, as with the SVM classifier, even better abuse detection rates with just approximated Private Set Intersection.

Given that data are replicated in multiple geo-graphically distributed data centers, and to meet the increasing requirements of distributed applications, many cloud data stores adopt eventual consistency and therefore allow to run data intensive operations under low latency. This comes at the cost of data staleness. In this work, we prioritize data replication based on a set of flexible data semantics that can best suit all types of Big Data applications, avoiding overloading both network and systems during large periods of disconnection or partitions in the network. Therefore, we integrated these data semantics into the core architecture of a well-known NoSQL data store (e.g., HBase), which leverages a three-dimensional vector-field mdel (i.e., regarding timeliness, number of pending updates and divergence bounds) to provision data selectively in an on-demand fashion to applications. This enhances the former consistency model by providing a number of required levels of consistency to different applications such as, social networks or ecommerce sites, where priority of updates also differ. In addition, our implementation of the model into HBase allows updates to be tagged and grouped atomically in logical batches, akin to transactions, ensuring atomic changes and correctness of updates as they are propagated.